Metabolomics and lipidomics of plasma biomarkers for tuberculosis diagnostics using UHPLC-HRMS
IntroductionDetermining metabolic profiles during host-pathogen interactions is crucial for developing novel diagnostic tests and exploring the mechanisms underlying infectious diseases. However, the characteristics of the circulating metabolites and their functions after Mycobacterium tuberculosis...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Cellular and Infection Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcimb.2025.1526740/full |
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| author | Gaofeng Sun Quan Wang Xinjie Shan Maierheba Kuerbanjiang Ruiying Ma Wensi Zhou Lin Sun Qifeng Li |
| author_facet | Gaofeng Sun Quan Wang Xinjie Shan Maierheba Kuerbanjiang Ruiying Ma Wensi Zhou Lin Sun Qifeng Li |
| author_sort | Gaofeng Sun |
| collection | DOAJ |
| description | IntroductionDetermining metabolic profiles during host-pathogen interactions is crucial for developing novel diagnostic tests and exploring the mechanisms underlying infectious diseases. However, the characteristics of the circulating metabolites and their functions after Mycobacterium tuberculosis infection have not been fully elucidated. Therefore, this study aimed to identify the differential metabolites in tuberculosis (TB) patients and explore the diagnostic value of these metabolites as potential biomarkers.MethodsSeventy-two TB patients and 78 healthy controls (HCs) were recruited as the training set, while 30 TB patients and 30 HCs were enrolled as the independent validation set. Metabolites in plasma samples were analyzed by high-resolution mass spectrometry. Differential metabolites were screened using principal component analysis and machine learning algorithms including LASSO, Random Forest, and XGBoost. The diagnostic accuracy of the core differential metabolites was evaluated. Pearson correlation analysis was performed.ResultThe metabolic profiling of TB patients showed significant separation from that of the HCs. In the training set, 282 metabolites were identified as differentially expressed in TB patients, with 214 metabolites validated in the independent validation cohort. KEGG pathway enrichment analysis showed that the differential metabolites were mainly enriched in lipid metabolism. Seven core differential metabolites were identified by the three machine learning algorithms. Receiver operating characteristic analysis revealed that Angiotensin IV had high accuracy in diagnosing TB.ConclusionThese newly identified plasma metabolites are expected to serve as potentially valuable biomarkers for TB, potentially facilitating the diagnosis of the disease and enhancing the understanding of its underlying mechanisms. |
| format | Article |
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| institution | OA Journals |
| issn | 2235-2988 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Cellular and Infection Microbiology |
| spelling | doaj-art-e97b3a21ea04446697f34af4fde3da302025-08-20T02:38:21ZengFrontiers Media S.A.Frontiers in Cellular and Infection Microbiology2235-29882025-06-011510.3389/fcimb.2025.15267401526740Metabolomics and lipidomics of plasma biomarkers for tuberculosis diagnostics using UHPLC-HRMSGaofeng Sun0Quan Wang1Xinjie Shan2Maierheba Kuerbanjiang3Ruiying Ma4Wensi Zhou5Lin Sun6Qifeng Li7Graduate of School, Xinjiang Medical University, Urumqi, ChinaDepartment of Medical Laboratory, The Infectious Disease Hospital of Xinjiang Uygur Autonomous Region, Urumqi, ChinaDepartment of Medical Laboratory, Xinjiang Institute of Pediatrics, Xinjiang Hospital of Beijing Children’s Hospital Children’s Hospital of Xinjiang Uygur Autonomous Region, The Seventh People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, ChinaGraduate of School, Xinjiang Medical University, Urumqi, ChinaDepartment of Medical Laboratory, The Infectious Disease Hospital of Xinjiang Uygur Autonomous Region, Urumqi, ChinaDepartment of Medical Laboratory, The Infectious Disease Hospital of Xinjiang Uygur Autonomous Region, Urumqi, ChinaLaboratory of Respiratory Diseases, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, Beijing Key Laboratory of Core Technologies for the Prevention and Treatment of Emerging Infectious Diseases in Children, Key Laboratory of Major Diseases in Children, Ministry of Education, National Clinical Research Center for Respiratory Diseases, National Center for Children’s Health, Beijing, ChinaDepartment of Science and Education, Xinjiang Institute of Pediatrics, Xinjiang Hospital of Beijing Children’s Hospital, Children’s Hospital of Xinjiang Uygur Autonomous Region, The Seventh People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, ChinaIntroductionDetermining metabolic profiles during host-pathogen interactions is crucial for developing novel diagnostic tests and exploring the mechanisms underlying infectious diseases. However, the characteristics of the circulating metabolites and their functions after Mycobacterium tuberculosis infection have not been fully elucidated. Therefore, this study aimed to identify the differential metabolites in tuberculosis (TB) patients and explore the diagnostic value of these metabolites as potential biomarkers.MethodsSeventy-two TB patients and 78 healthy controls (HCs) were recruited as the training set, while 30 TB patients and 30 HCs were enrolled as the independent validation set. Metabolites in plasma samples were analyzed by high-resolution mass spectrometry. Differential metabolites were screened using principal component analysis and machine learning algorithms including LASSO, Random Forest, and XGBoost. The diagnostic accuracy of the core differential metabolites was evaluated. Pearson correlation analysis was performed.ResultThe metabolic profiling of TB patients showed significant separation from that of the HCs. In the training set, 282 metabolites were identified as differentially expressed in TB patients, with 214 metabolites validated in the independent validation cohort. KEGG pathway enrichment analysis showed that the differential metabolites were mainly enriched in lipid metabolism. Seven core differential metabolites were identified by the three machine learning algorithms. Receiver operating characteristic analysis revealed that Angiotensin IV had high accuracy in diagnosing TB.ConclusionThese newly identified plasma metabolites are expected to serve as potentially valuable biomarkers for TB, potentially facilitating the diagnosis of the disease and enhancing the understanding of its underlying mechanisms.https://www.frontiersin.org/articles/10.3389/fcimb.2025.1526740/fullTuberculosismetaboliteUHPLC-HRMSdiagnosisbiomarkermachine learning |
| spellingShingle | Gaofeng Sun Quan Wang Xinjie Shan Maierheba Kuerbanjiang Ruiying Ma Wensi Zhou Lin Sun Qifeng Li Metabolomics and lipidomics of plasma biomarkers for tuberculosis diagnostics using UHPLC-HRMS Frontiers in Cellular and Infection Microbiology Tuberculosis metabolite UHPLC-HRMS diagnosis biomarker machine learning |
| title | Metabolomics and lipidomics of plasma biomarkers for tuberculosis diagnostics using UHPLC-HRMS |
| title_full | Metabolomics and lipidomics of plasma biomarkers for tuberculosis diagnostics using UHPLC-HRMS |
| title_fullStr | Metabolomics and lipidomics of plasma biomarkers for tuberculosis diagnostics using UHPLC-HRMS |
| title_full_unstemmed | Metabolomics and lipidomics of plasma biomarkers for tuberculosis diagnostics using UHPLC-HRMS |
| title_short | Metabolomics and lipidomics of plasma biomarkers for tuberculosis diagnostics using UHPLC-HRMS |
| title_sort | metabolomics and lipidomics of plasma biomarkers for tuberculosis diagnostics using uhplc hrms |
| topic | Tuberculosis metabolite UHPLC-HRMS diagnosis biomarker machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fcimb.2025.1526740/full |
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